Analysis device, analysis method and program

Abstract

To provide an analysis device for accurately predicting effects of a campaign under appropriate refinement.SOLUTION: An analysis device includes; a first processing part for inputting campaign content information and a target intermediary into a first model that when the campaign content information and the intermediary are input, outputs a provisional transaction volume or a transaction amount, so as to derive the provisional transaction volume or transaction amount; a second processing part for inputting the derived provisional transaction volume or transaction amount and the target intermediary to a second model that when the provisional transaction volume or transaction amount and the intermediary are input, outputs a settlement error occurrence rate, so as to derive the settlement error occurrence rate; and a third processing part for discounting the derived provisional transaction volume or transaction amount by the derived settlement error occurrence rate to derive a final transaction volume or transaction amount.SELECTED DRAWING: Figure 9

Description

[Technical field]

[0001] The present invention relates to an analysis device, an analysis method, and a program. [Background technology]

[0002] Conventionally, in the field of electronic payment, benefits are provided to users in the form of coupons or points, which are limited to a certain period or area. A unit of service that provides such benefits is sometimes called a campaign. Patent Document 1 describes campaigns. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-35656 A Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional techniques, it was sometimes impossible to accurately predict the effectiveness of a campaign with appropriate narrowing down.

[0005] The present invention has been made in consideration of the above circumstances, and one of its objectives is to provide an analysis device, an analysis method, and a program that can accurately predict the effectiveness of a campaign with appropriate narrowing down. [Means for solving the problem]

[0006] One embodiment of the present invention is an analysis device that analyzes the effectiveness of a campaign in an electronic payment service, the analysis device comprising: an acquisition unit that acquires information on the transaction volume or transaction amount for each intermediary that mediates electronic payments and information on the occurrence of payment errors; a reception unit that accepts input of campaign content information and target intermediaries; a first processing unit that derives the provisional transaction volume or transaction amount by inputting the campaign content information and the target intermediary whose input has been accepted into a first model that outputs a provisional transaction volume or transaction amount when the campaign content information and the intermediary are input; a second processing unit that derives the payment error occurrence rate by inputting the derived provisional transaction volume or transaction amount and the target intermediary into a second model that outputs a payment error occurrence rate when the provisional transaction volume or transaction amount and the intermediary are input; and a third processing unit that discounts the derived provisional transaction volume or transaction amount by the derived payment error occurrence rate to derive a final transaction volume or transaction amount.

[0007] Another aspect of the present invention is an analysis device that analyzes the effectiveness of a campaign in an electronic payment service, the analysis device comprising: an acquisition unit that acquires information regarding the transaction volume or transaction amount for each area for each intermediary that mediates electronic payments; a reception unit that accepts input of campaign content information, target intermediaries, and target areas; and a processing unit that derives the transaction volume or transaction amount by inputting the campaign content information, target intermediaries, and target areas into a model that outputs the transaction volume or transaction amount in response to input of the campaign content information, intermediaries, and target areas. Effect of the Invention

[0008] According to one aspect of the present invention, it is possible to provide an analysis device, an analysis method, and a program that can accurately predict the effectiveness of a campaign with appropriate narrowing down. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an example of a configuration for realizing an electronic payment service. [Diagram 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payment. [Diagram 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payment. [Figure 4] 1 is a configuration diagram of a payment server 100 according to a first embodiment. [Diagram 5] FIG. 13 is a diagram showing an example of the contents of user information 172. [Figure 6] FIG. 13 is a diagram showing an example of the contents of affiliated store / store information 176. [Figure 7] FIG. 13 is a diagram showing an example of the contents of payment statistical information 178 according to the first embodiment. [Figure 8] FIG. 11 is a diagram showing an example of the contents of campaign information 180 according to the first embodiment. [Figure 9] 1 is a configuration diagram of an analysis device 200 according to a first embodiment. [Figure 10] FIG. 13 is a diagram showing an example of a learning process of a first model 274. [Figure 11] FIG. 13 is a diagram showing an example of a learning process of a second model 276. [Figure 12] FIG. 13 is a diagram showing an example of the contents of settlement statistical information 178 according to the second embodiment. [Figure 13] FIG. 13 is a diagram showing an example of the contents of campaign information 180 according to the second embodiment. [Figure 14] FIG. 13 is a diagram showing an example of the contents of payment statistical information 178 according to the second embodiment. [Figure 15] FIG. 11 is a diagram showing an example of the contents of campaign information 180 according to the second embodiment. [Figure 16] FIG. 11 is a configuration diagram of an analysis device 300 according to a second embodiment. [Figure 17] FIG. 13 is a diagram showing the relationship between the transaction amount for each intermediary and the breakdown by district. [Figure 18] FIG. 13 is a diagram showing an example of a learning process of the model 374. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of an analysis device, an analysis method, and a program of the present invention will be described with reference to the drawings. Various devices for providing services to users and performing internal analysis, such as a "server," "management device," and "information providing device" that will appear below, may be realized by a distributed device group, and each device may be operated by a different business operator. In addition, the owner of the device hardware (the cloud server provider) and the business operator that actually operates the device may also be different.

[0011] The analysis device operates in association with a payment server that provides an electronic payment service. The analysis device may be built into the payment server or may be a device separate from the payment server. In the following description, it is assumed that the analysis device is a separate device. An application program that operates on a user's terminal device and the payment server work together to provide an electronic payment service. In the following description, the application program is referred to as a payment app. The electronic payment service is a service that supports payments related to the purchase of goods and services at a store. The store is, for example, a physical store (real store) that exists in real space, but may also include a virtual store for electronic commerce. The virtual store may include a store provided by an entity other than the operator of the electronic payment service. In that case, when making a payment for shopping at the virtual store, the screen is controlled to transition to an interface screen of the electronic payment service. In the electronic payment service, the store is treated as belonging to, for example, an affiliated store (brand), and processing such as payment when a purchase is made at the store is mainly performed between the user and the affiliated store. Alternatively, processing such as payment may be performed between the user and the store.

[0012] First Embodiment [Electronic payment service] Fig. 1 is a diagram showing an example of a configuration for realizing an electronic payment service. The electronic payment service is realized mainly by a payment server 100. The payment server 100 communicates with, for example, one or more user terminal devices 10, one or more first store terminal devices 50, and one or more second store terminal devices 70 via a network NW. The network NW includes, for example, the Internet, a LAN (Local Area Network), a wireless base station, a provider device, and the like.

[0013] The user terminal device 10 is, for example, a portable terminal device such as a smartphone or a tablet terminal. The user terminal device 10 is a computer device having at least an optical reading function, a communication function, a display function, an input reception function, and a program execution function. In the following description, components for realizing these functions are referred to as a camera, a communication device, a touch panel, a CPU (Central Processing Unit), etc. In the user terminal device 10, a processor such as a CPU executes a payment application 20, thereby operating to provide an electronic payment service to a user in cooperation with a payment server 100. The payment application 20 is installed in the user terminal device 10 from, for example, an application store, and controls the camera, communication device, touch panel, etc.

[0014] The first store terminal device 50 is installed, for example, in a store. The first store terminal device 50 is a computer device having at least a product price acquisition function, an optical reading function, a program execution function, and a communication function. The first store terminal device 50 includes a so-called POS (Point of Sale) device, and the product price acquisition function and the optical reading function may be realized by the POS device. The store code image 60 is placed in the store, and is a code image such as a QR code (registered trademark) printed on a paper or plastic medium. The store code image 60 may be displayed on a display placed in the store (which may be the display of a terminal device such as a smartphone).

[0015] The second store terminal device 70 is used by the operator of the affiliated store. The second store terminal device 70 is a smartphone, a tablet terminal, a personal computer, or the like. An interface 72 for affiliated stores runs on the second store terminal device 70. The interface 72 for affiliated stores may be an app for affiliated stores or a browser. The interface 72 for affiliated stores accepts coupon settings and the like made by the operator of the affiliated store and transmits them to the payment server 100. The second store terminal device 70, which is a smartphone, has the function of displaying a code image corresponding to a store code image and reading a code image displayed by the user terminal device 10 by executing the app for affiliated stores.

[0016] The payment server 100 realizes electronic payment based on payment information received from the user terminal device 10 or the first store terminal device 50. The first store terminal device 50 may include a POS device and an affiliated store server, in which case the payment information is sent from the POS device via the affiliated store server to the payment server 100. In the following explanation, no distinction is made between these two and it is assumed that the payment information is sent from the first store terminal device 50.

[0017] 2 and 3 are sequence diagrams illustrating the general flow of electronic payment. There may be two patterns of electronic payment: pattern 1 and pattern 2.

[0018] In the case of pattern 1 (hereinafter referred to as user scan) shown in FIG. 2, the user terminal device 10 with the payment application 20 activated decodes the store code image 60 by the optical reading function (S1). The store code image 60 includes store URL (Uniform Resource Locator) information. This store URL is an electronic payment service domain to which information capable of identifying a store is added, and is associated with an affiliated store ID, a store ID, etc. in the payment server 100 (described later). The payment application 20 transmits the first payment information including the store URL and the account ID to the payment server 100 (S2). The payment server 100 searches for store information (described later) from the affiliated store ID and the store ID corresponding to the store URL, acquires the affiliated store name and the store name information (S3), and transmits it to the payment application 20 (S4). The user inputs the payment amount into the user terminal device 10 on the screen on which the affiliated store name and the store name are displayed (S5). Then, the user terminal device 10 generates second payment information including at least the payment amount, and transmits it to the payment server 100 (S6). The payment server 100 performs electronic payment based on the received second payment information (S7). The payment server 100 then transmits a payment completion notice (information for displaying a payment completion screen) to the payment application 20 (S8), and the payment application 20 displays the payment completion screen (S9). Note that when the store code image 60 is displayed on a display installed in the store, the store code image 60 may include not only the store URL but also information on the payment amount. In this case, the step of the user inputting the payment amount is omitted, and the information on the payment amount is included in the first payment information and transmitted to the payment server 100. Information on the affiliated store name and the store name may be included in the payment completion screen and displayed.

[0019] In the case of pattern 2 (hereinafter referred to as store scan) shown in FIG. 3, when the payment application 20 is started, when a payment operation is performed in the payment application 20, when an automatic update timing (e.g., every minute) occurs, and at other timings, the payment application 20 transmits a request for issuing a one-time code to the payment server 100 (S11). The payment server 100 generates a one-time code (S12) and transmits it to the payment application 20 (S13). The payment application 20 displays a code image such as a QR code or a barcode generated based on the one-time code (S14). The user holds (presents) the display surface of the user terminal device 10 over the first store terminal device 50, and the first store terminal device 50 decodes the code image by an optical reading function and obtains the one-time code, etc. (S15). The first store terminal device 50 then generates payment information including the one-time code, payment amount, affiliated store ID, store ID, etc., and transmits it to the payment server 100 (S16). The payment amount information is acquired in advance by reading a barcode or manually entering it. The payment server 100 identifies the user corresponding to the one-time code based on the received information and performs electronic payment (S17). The payment server 100 then transmits a payment completion notice to the payment application 20 (S18), and the payment application 20 displays a payment completion screen (S19).

[0020] Note that electronic payment may be performed using only one of the above patterns. Furthermore, the "account ID" described in FIG. 2 may be other information (e.g., a phone number) that can be used as user identification information. Furthermore, issuing a one-time code may be omitted in the store scan, and the payment application 20 may display a code image generated based on the user's account ID. In this case, the payment server 100 identifies the user corresponding to the account ID instead of identifying the user corresponding to the one-time code.

[0021] [Payment server] FIG. 4 is a configuration diagram of the payment server 100 according to the first embodiment. The payment server 100 includes, for example, a communication unit 110, a payment content providing unit 120, a payment processing unit 130, an information management unit 140, and a storage unit 170. The components other than the communication unit 110 and the storage unit 170 are realized by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by cooperation between software and hardware. The program may be stored in advance in a storage device (storage device having a non-transient storage medium) such as an HDD (Hard Disk Drive) or a flash memory, or may be stored in a removable storage medium (non-transient storage medium) such as a DVD or a CD-ROM, and may be installed in the storage device by mounting the storage medium in a drive device.

[0022] The storage unit 170 is a HDD, a flash memory, a RAM (Random Access Memory), etc. The storage unit 170 may be a NAS (Network Attached Storage) device that the payment server 100 can access via a network. The storage unit 170 stores information such as user information 172, payment content information 174, affiliated store / shop information 176, payment statistics information 178, and campaign information 180.

[0023] The communication unit 110 is a communication interface for connecting to the network NW. The communication unit 110 is, for example, a network interface card.

[0024] The payment content providing unit 120 has, for example, a function of a Web server, and provides information (content) for displaying various screens of the electronic payment service to the user terminal device 10. The payment content providing unit 120 appropriately reads necessary content from the payment content information 174 and provides it to the user terminal device 10. The user terminal device 10 accepts various inputs by the user while the content is being played by the payment application 20, and transmits the above-mentioned payment information and the like to the payment server 100.

[0025] The payment processing unit 130 performs payment processing based on the payment information transmitted by the user terminal device 10 or the first store terminal device 50. The payment processing unit 130 performs payment processing while referring to the user information 172.

[0026] FIG. 5 is a diagram showing an example of the contents of the user information 172. The user information 172 is an example of the registration information of a user. The user information 172 is, for example, a user URL, an account ID, a telephone number, a password, as well as information associated with an email address, a user ID, a name, an address, a date of birth, a registration date, a charge balance, a post-payment setting, a post-payment limit, a post-payment usage amount, a post-payment available amount, a payment method setting, a bank account, a credit card number, a charge history information, and a payment history information. The user URL is used for a remittance process between users. When registering for the electronic payment service, it is necessary to register a telephone number and a password. The account ID is issued to the user by the payment server 100, and the user ID is an ID that can be set by the user at will (does not have to be set). Similarly, the email address, and the name, address, and date of birth are information that can be set by the user at will (does not have to be set). The registration date is the date on which the user registered for the electronic payment service (the date on which the account was created). Hereinafter, the user's instance (electronic payment account) to which this information is associated will be referred to as an account.

[0027] The charge balance is information indicating the balance of electronic money that is set by a user by transferring money to an account in advance. Methods of transfer include transfer from an ATM (Automatic Teller Machine) of a designated company (bank) and transfer from a registered bank account. Deferred payment setting is information indicating whether or not the setting for enabling electronic payment by deferred payment has been completed, and is set to either "completed" or "not completed." Further details of deferred payment will be described later. The payment method setting is setting information indicating whether the user will make electronic payment from the charge balance at that time or payment by deferred payment. The bank account and credit card number are information on a bank account or credit card number (account number, card number) that can be deposited into an electronic payment service. The charge history information is a history of the user transferring money to an electronic payment service in advance to increase the charge balance. The payment history information is information indicating the details of the payment made by the user (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.) for each payment.

[0028] 6 is a diagram showing an example of the contents of affiliated store / store information 176. The affiliated store / store information 176 includes, for example, a first table 176A in which an affiliated store ID and a store ID are associated with a store URL, a second table 176B in which an affiliated store name and sales amount (described above) are associated with an affiliated store ID, and a third table 176C in which a store ID is associated with a store name. In addition to this information, the affiliated store / store information 176 may also include information such as the category of the affiliated store or store, the location of the store, and payment patterns.

[0029] The information management unit 140 manages user information 172 and affiliated store / store information 176 based on information acquired from the user terminal device 10 and the second store terminal device 70. The information management unit 140 adds, edits, and deletes new records for the user information 172 and affiliated store / store information 176. The information management unit 140 also edits payment statistics information 178 and campaign information 180, and transmits them to the analysis device 200 as appropriate.

[0030] FIG. 7 is a diagram showing an example of the contents of payment statistics information 178 according to the first embodiment. In the payment statistics information 178, for example, information such as transaction volume, transaction amount, and number of payment errors is associated with identification information of an intermediary for each observation period. The observation period is, for example, one month or one week. An intermediary is a business entity that mediates electronic payments, and includes, for example, affiliated stores and electronic payment platform providers. A payment error is an event such as a communication error, insufficient balance, or adding an item to a cart on a shopping site but not making a payment.

[0031] Fig. 8 is a diagram showing an example of the contents of campaign information 180 according to the first embodiment. In the campaign information 180, for example, campaign content information, transaction volume, transaction amount, number of payment errors, and other information are associated with the identification information of an intermediary. The campaign content information specifies the target period, the return rate, and the like. The transaction volume and transaction amount are calculated by tallying up the number and amount of transactions during the period in which the campaign was conducted.

[0032] [Electronic payment] When payment information is acquired from the user terminal device 10 or the first store terminal device 50, the payment processing unit 130 refers to the user information 172 to acquire the "payment method setting" of the user. For a user whose "payment method setting" is set to "charge balance", the payment processing unit 130 performs electronic payment as follows. For example, the payment processing unit 130 performs electronic payment by decreasing the charge balance managed in association with the user ID and increasing the item value of the affiliated store's sales. The item value of the affiliated store's sales is not used as electronic money itself, for example, and an amount corresponding to the item value of the sales is transferred to a bank account in a cycle according to an agreement between the affiliated store and the electronic payment service.

[0033] The payment processing unit 130 performs electronic payment for users whose "setting information" is set to "deferred payment" as follows. Deferred payment is set separately from "credit payment" in cooperation with a credit card company, which is a separate entity from the operator of the electronic payment service, and the operator of the credit card company that cooperates with the electronic payment service is the creditor, and electronic payment that does not depend on the charge balance within the deferred payment limit is permitted. In order to receive the deferred payment service, it may be required to obtain a credit card provided by the operator of the electronic payment service. The amount used for deferred payment is settled on the payment date of the following month in a lump sum for one month, for example, by debiting from a bank account. In this case, the payment processing unit 130 performs provisional payment by adding the payment amount to the deferred payment amount and subtracting the same amount from the deferred payment available amount, and when the closing date comes, it performs processing to debit the payment for the current month on the payment date of the following month as described above, or requests the operator of the credit card company to perform such processing. In addition, if the payment amount exceeds the deferred payment available amount at the time of provisional payment, an error notification is returned to the payment application 20.

[0034] First Embodiment [Analysis equipment] The analysis device 200 will be described below. FIG. 9 is a configuration diagram of the analysis device 200 according to the first embodiment. The analysis device 200 includes, for example, an acquisition unit 210, a reception unit 220, a first processing unit 230, a second processing unit 240, a third processing unit 250, and a storage unit 270. The components other than the storage unit 270 are realized by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including a circuit unit; circuitry), or may be realized by cooperation between software and hardware. The program may be stored in a storage device such as an HDD or a flash memory (a storage device having a non-transient storage medium) in advance, or may be stored in a removable storage medium such as a DVD or a CD-ROM (a non-transient storage medium), and may be installed in the storage device by mounting the storage medium in a drive device.

[0035] The storage unit 270 stores information such as analysis source information 272, a first model 274, and a second model 276.

[0036] An input / output device 290 may be connected to the analysis device 200. The input / output device 290 includes a keyboard, a mouse, a touch pad, a touch panel, a display device, and the like.

[0037] The acquisition unit 210 acquires the payment statistical information 178 and the campaign information 180 from the payment server 100. The intermediaries from which the payment statistical information 178 can be acquired may be a part of the intermediaries to be analyzed. The acquisition unit 210 may generate a model for complementing the payment statistical information 178 based on data of the intermediaries from which the payment statistical information 178 can be acquired, and for the intermediaries from which the payment statistical information 178 cannot be acquired, may generate complementary data by applying the acquireable data to the model. The reception unit 220 receives input of content information of the campaign to be analyzed and the target intermediaries. The reception unit 220 may receive the above input based on an operation on the input / output device 290, or may receive input from another device via communication.

[0038] The first processing unit 230 derives a provisional transaction volume or transaction amount by inputting the content information of the campaign and the target intermediary, whose input has been accepted, into the first model 274. Note that the content information of the campaign is subjected to pre-processing to be normalized and converted into abstracted feature information before being input into the first model 274 (the same applies in the learning process). FIG. 10 is a diagram showing an example of the learning process of the first model 274. The first model 274 is a model that has been trained to output an estimated transaction volume or transaction amount when the content information of the campaign and the intermediary are input by a method such as backpropagation, using the content information of the campaign and the intermediary obtained from the campaign information 180 as learning data and the transaction volume or transaction amount when the campaign is carried out as teacher data. The transaction volume or transaction amount output by the first model 274 is, for example, TPS (Transactions per second), that is, the transaction volume or transaction amount per unit time (for example, 1 second). By adding this over time, the overall transaction volume or transaction amount due to the influence of the campaign can be derived.

[0039] The second processing unit 240 derives the settlement error rate by inputting the provisional transaction volume or transaction amount derived by the first processing unit 230 and the target intermediary into the second model 276 (note that deriving the number of settlement errors is equivalent to this). FIG. 11 is a diagram showing an example of the learning process of the second model 276. The second model 276 is a model trained to output a settlement error rate when the transaction volume or transaction amount and the intermediary are input by a method such as backpropagation, using the transaction volume or transaction amount and the intermediary obtained from the settlement statistics information 178 as learning data and the settlement error occurrence rate at that time as teacher data.

[0040] The third processing unit 250 discounts the provisional trading volume or trading amount derived by the first processing unit 230 by the settlement error occurrence rate derived by the second processing unit 240 to derive a final trading volume or trading amount. This final trading volume or trading amount becomes the trading volume or trading amount expected based on the input campaign content information and the target intermediaries. Information on the final trading volume or trading amount may be displayed by the input / output device 290 or provided to another device via a network. The third processing unit 250 may also sum the derived values ​​between intermediaries to derive the total trading volume or total trading amount in the case where the campaign is implemented, and display or provide it as described above.

[0041] 12 and 13 are diagrams for explaining the effects of this embodiment. FIG. 12 is a graph showing the transaction volume for each major intermediary in a certain observation period (which may be the same as above or different). FIG. 13 is a graph showing the number of settlement errors for each of the same intermediaries in the same observation period as FIG. 12. As shown in the figure, the number of settlement errors is not proportional to the transaction volume, and the characteristics differ depending on the intermediary. There is a mixture of intermediaries with different characteristics, such as those in which the number of settlement errors increases as the transaction volume increases, and those in which the correlation between the transaction volume and the number of settlement errors is relatively low. Since the second model 276 has learned such characteristics in advance, it can output an accurate settlement error occurrence rate when a provisional transaction volume or transaction amount and a target intermediary are input.

[0042] According to the first embodiment described above, it is possible to accurately predict the effectiveness of a campaign with appropriate narrowing down.

[0043] <Second embodiment> The second embodiment will be described below. In the second embodiment, the contents of the payment statistical information 178 and the campaign information 180 are different from those in the first embodiment, and the configuration and functions of the analysis device are also different. The following description will focus on the differences.

[0044] Fig. 14 is a diagram showing an example of the contents of payment statistical information 178 according to the second embodiment. Fig. 15 is a diagram showing an example of the contents of campaign information 180 according to the second embodiment. The payment statistical information 178 and campaign information 180 according to the second embodiment are information obtained by further subdividing the payment statistical information 178 and campaign information 180 according to the first embodiment into areas where payments were made. Note that the number of payment errors is not essential in the second embodiment, and is therefore not shown in the figures.

[0045] FIG. 16 is a configuration diagram of an analysis device 300 according to the second embodiment. The analysis device 300 includes, for example, an acquisition unit 310, a reception unit 320, a processing unit 330, and a storage unit 370. The components other than the storage unit 370 are realized by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including a circuit unit; circuitry), or may be realized by cooperation between software and hardware. The program may be stored in a storage device such as an HDD or flash memory (a storage device having a non-transient storage medium) in advance, or may be stored in a removable storage medium such as a DVD or CD-ROM (a non-transient storage medium), and may be installed in the storage device by mounting the storage medium in a drive device.

[0046] The acquisition unit 310 acquires the payment statistical information 178 and the campaign information 180 from the payment server 100. The reception unit 320 receives input of content information of the campaign to be analyzed, the target intermediary, and the target district. The reception unit 320 may receive the above input based on an operation on the input / output device 390, or may receive input from another device via communication.

[0047] The processing unit 330 derives the transaction volume or transaction amount by inputting the content information of the campaign, the target intermediary, and the target district, whose input has been accepted, into the model 374. FIG. 17 is a diagram showing an example of the learning process of the model 374. The model 374 is a model that has been trained to output an estimated transaction volume or transaction amount when the content information of the campaign, the intermediary, and the target district are input by a method such as backpropagation, using the content information of the campaign, the intermediary, and the district obtained from the campaign information 180 as learning data, and the transaction volume or transaction amount when the campaign is carried out as teacher data. The transaction volume or transaction amount output by the model 374 is, for example, TPS, that is, the transaction volume or transaction amount per unit time (for example, 1 second), similar to the first model 274. By adding these over time, the overall transaction volume or transaction amount due to the influence of the campaign can be derived. The processing unit 330 may sum the derived values ​​between intermediaries and / or between districts to derive the total transaction volume or total transaction amount when the campaign is carried out.

[0048] FIG. 18 is a diagram showing the relationship between the transaction amount for each intermediary and the breakdown by district. As shown in the figure, the districts with large transaction amounts differ for each intermediary, and it is desirable to take that ratio into consideration when predicting the effectiveness of a campaign. Model 374 was trained by including districts in the training data, so it is possible to accurately output which intermediary and in which district a campaign will be most effective.

[0049] According to the second embodiment described above, it is possible to accurately predict the effectiveness of a campaign with appropriate narrowing down.

[0050] In the first embodiment, the information segmented by district may be used as in the second embodiment, and the district may be further input to at least one of the first model 274 and the second model 276. That is, the acquisition unit 210 acquires information on the transaction volume or transaction amount for each district for each intermediary, the reception unit 220 further receives input of the target district, the first model 274 is a model that outputs a provisional transaction volume or transaction amount when the content information of the campaign, the intermediary, and the target district are input, and the first processing unit 230 may derive the transaction volume or transaction amount by further inputting the target district whose input has been received into the first model 274. The second model 276 is a model that outputs a settlement error occurrence rate when the provisional transaction volume or transaction amount, the intermediary, and the target district are input, and the second processing unit 240 may derive the settlement error occurrence rate by further inputting the target district whose input has been received into the second model 276. Both of them may be implemented at the same time. In this way, the effect of the campaign can be predicted more accurately.

[0051] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0052] 10 User terminal device 20. Payment App 100 payment servers 120 Payment Contents Provider 130 Payment processing unit 140 Information Management Department 200, 300 analysis equipment 210, 310 Acquisition Department 220, 320 Reception 230 First Processing Section 240 Second Processing Section 250 Third Processing Section 270, 370 storage section 274 1st Model 276 2nd Model 330 Processing section 374 Models

Claims

1. An analysis device for analyzing the effectiveness of a campaign in an electronic payment service, comprising: an acquisition unit that acquires information regarding the transaction volume or transaction amount for each intermediary that mediates electronic settlement, and information regarding the occurrence of a settlement error; a reception unit for receiving input of campaign content information and target intermediaries; a first processing unit that inputs campaign content information and a target intermediary to a first model that outputs a provisional trading volume or trading amount when the campaign content information and the intermediary are input, and derives the provisional trading volume or trading amount by inputting the campaign content information and the target intermediary that have been accepted; a second processing unit that derives the settlement error occurrence rate by inputting the derived provisional transaction volume or transaction amount and the target intermediary into a second model that outputs a settlement error occurrence rate when a provisional transaction volume or transaction amount and an intermediary are input; a third processing unit that discounts the derived provisional transaction volume or transaction amount by the derived settlement error occurrence rate to derive a final transaction volume or transaction amount; An analysis device comprising:

2. The acquisition unit acquires information regarding a transaction volume or transaction amount for each area of ​​the intermediary, The reception unit further receives input of a target area, The first model is a model that outputs a provisional transaction volume or transaction amount when campaign content information, an intermediary, and a target area are input, The first processing unit further inputs the target district for which the input was accepted into the first model, thereby deriving the transaction volume or transaction amount. The analysis device according to claim 1.

3. The acquisition unit acquires information regarding a transaction volume or transaction amount for each area of ​​the intermediary, The reception unit further receives input of a target area, The second model is a model that outputs a settlement error occurrence rate when a provisional transaction volume or transaction amount, an intermediary, and a target area are input, The second processing unit further inputs the target district for which the input was accepted into the second model, thereby deriving the payment error occurrence rate.

3. The analysis device according to claim 1 or 2.

4. The third processing unit sums the derived values ​​among the intermediaries to derive a total transaction volume or a total transaction amount when the campaign is implemented.

3. The analysis device according to claim 1 or 2.

5. An analysis device for analyzing the effectiveness of a campaign in an electronic payment service, comprising: an acquisition unit that acquires information regarding transaction volume or transaction amount by region for each intermediary that mediates electronic payments; a reception unit for receiving input of campaign content information, target intermediaries, and target districts; a processing unit that inputs campaign content information, a target intermediary, and a target area to a model that outputs a trading volume or a trading amount when the campaign content information, the intermediary, and the target area are input, and derives the trading volume or the trading amount by inputting the campaign content information, the target intermediary, and the target area that have been received; An analysis device comprising:

6. The processing unit sums the derived values ​​between the intermediaries and between the districts to derive a total transaction volume or a total transaction amount when the campaign is implemented. The analysis device according to claim 5.

7. An analysis device that analyzes the effectiveness of campaigns in electronic payment services, A process of acquiring information regarding the transaction volume or transaction amount for each intermediary that mediates electronic payments, and information regarding the occurrence of payment errors; A process of receiving input of campaign content information and target intermediaries; a process of inputting campaign content information and a target intermediary into a first model that outputs a provisional trading volume or trading amount when the campaign content information and the intermediary are input, thereby deriving the provisional trading volume or trading amount; a process of deriving the settlement error occurrence rate by inputting the derived provisional transaction volume or transaction amount and the target intermediary into a second model that outputs a settlement error occurrence rate when a provisional transaction volume or transaction amount and an intermediary are input; A process of deriving a final transaction volume or transaction amount by discounting the derived provisional transaction volume or transaction amount by the derived settlement error occurrence rate; The analysis method to be performed.

8. An analysis device that analyzes the effectiveness of campaigns in electronic payment services, obtaining information regarding transaction volume or transaction value by district for each intermediary that mediates electronic payments; A process of accepting input of campaign content information, target intermediaries, and target districts; A process of inputting campaign content information, a target intermediary, and a target area into a model that outputs a trading volume or a trading amount when the campaign content information, the intermediary, and the target area are input, thereby deriving the trading volume or the trading amount; The analysis method to be performed.

9. An analysis device that analyzes the effectiveness of campaigns for electronic payment services. A process of acquiring information regarding the transaction volume or transaction amount for each intermediary that mediates electronic payments, and information regarding the occurrence of payment errors; A process of receiving input of campaign content information and target intermediaries; a process of inputting campaign content information and a target intermediary into a first model that outputs a provisional trading volume or trading amount when the campaign content information and the intermediary are input, thereby deriving the provisional trading volume or trading amount; a process of deriving the settlement error occurrence rate by inputting the derived provisional transaction volume or transaction amount and the target intermediary into a second model that outputs a settlement error occurrence rate when a provisional transaction volume or transaction amount and an intermediary are input; A process of deriving a final transaction volume or transaction amount by discounting the derived provisional transaction volume or transaction amount by the derived settlement error occurrence rate; A program for executing the above.

10. An analysis device that analyzes the effectiveness of campaigns for electronic payment services. obtaining information regarding transaction volume or transaction value by district for each intermediary that mediates electronic payments; A process of accepting input of campaign content information, target intermediaries, and target districts; A process of inputting campaign content information, a target intermediary, and a target area into a model that outputs a trading volume or a trading amount when the campaign content information, the intermediary, and the target area are input, thereby deriving the trading volume or the trading amount; A program for executing the above.

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